files = []
for fi in range(nfile):
filename = filedir+diag+'.'+yrstr[fi]+'ann.nc'
files.append(filename)
ts = pp.ts_multi_files(files,var[vi],0)
if zm:
ts = np.nanmean(ts,-1)
#%%
if init and vi==0:
outfile = outdir+'dim.'+simo[i]+'.npz'
fio.save(outfile,lat=lat,lon=lon,phalf=phalf,land_mask=land_mask,year=yr)
if init3d:
tmp = nc.netcdf_file(files[-1],'r',mmap=True)
pfull = tmp.variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+simo[i]+'.npz'
fio.save(outfile,pfull=pfull)
if init3dp:
tmp = nc.netcdf_file(files[-1],'r',mmap=True)
level = tmp.variables['level'][:].astype(np.float64)
outfile = outdir+'dim.'+simo[i]+'.npz'
fio.save(outfile,level=level)
for flagi in range(nflag):
ts_flag = ts
outdir_sub='ts/'+sub_dict[flag[flagi]]+'/'
if (flag[flagi] != ''):
ts_flag = pp.month_to_year(ts,flag[flagi])
outfile = outdir+outdir_sub+diago+'.'+timeo+'.'+simo[i]+'.npz'
fio.save(outfile,**{varo[vi]:ts_flag})
print var[vi]
lon = fs[-1].variables['lon'][:].astype(np.float64)
nlat = np.size(lat)
nlon = np.size(lon)
#phalf = fs[-1].variables['phalf'][:].astype(np.float64)
#zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)
if ('land_mask' in fs[-1].variables):
land_mask = fs[-1].variables['land_mask'][:].astype(np.float64)
fs[-1].close()
#%%
filedir = atmdir+indir_sub
files = []
for fi in range(nfile):
filename = filedir+diag+'.'+yrstr[fi]+'.nc'
files.append(filename)
ts = pp.ts_multi_files(files,var,0)
ts = np.array(ts)
#%%
if (flag != ''):
ts = pp.month_to_year(ts,yr_ts,flag)
if zm:
ts_zon = np.mean(ts,-1)
if gm:
area = grid.calcGridArea(lat,lon)
area.shape = [1,nlat,nlon]
ts_glb = np.sum(ts*area,-1)
ts_glb = np.sum(ts_glb,-1)
ts_glb = ts_glb/np.sum(area)
print ts_glb
plt.plot(ts_zon[0,:]-ts_zon[0,0])
plt.legend(pert,fontsize=10)
uMSE = np.sum(MSEMon * uMon * dpMon, 0) / GRAV
uMSEMC[yri * 12 + moni, :, :] = uMSE
wMC[yri * 12 + moni, :, :, :] = np.mean(omega, 0)
wMSEbot[yri * 12 +
moni, :, :] = (np.mean(omega * MSE, 0)[-1, ...]) / GRAV
wMSEtop[yri * 12 +
moni, :, :] = (np.mean(omega * MSE, 0)[0, ...]) / GRAV
#%%
#mind = [6,7,8]
"""
if init:
outfile = outdir+'dim.'+pert_dict[pert[i]]+'.npz'
fio.save(outfile,lat=lat,lon=lon,phalf=phalf,land_mask=land_mask,year=yr)
"""
for flagi in range(nflag):
vhTot = pp.month_to_year(vMSETot, yr_ts, flag[flagi]) #*20650/1e19
vhMC = pp.month_to_year(vMSEMC, yr_ts, flag[flagi])
vhTr = pp.month_to_year(vMSETr, yr_ts, flag[flagi])
uhTot = pp.month_to_year(uMSETot, yr_ts, flag[flagi])
uhMC = pp.month_to_year(uMSEMC, yr_ts, flag[flagi])
uhTr = pp.month_to_year(uMSETr, yr_ts, flag[flagi])
whbot = pp.month_to_year(wMSEbot, yr_ts, flag[flagi])
whtop = pp.month_to_year(wMSEtop, yr_ts, flag[flagi])
net = pp.month_to_year(net_atm, yr_ts, flag[flagi])
de = pp.month_to_year(dene, yr_ts, flag[flagi])
"""
outdir_sub='ts/'+sub_dict[flag[flagi]]+'/'
outfile = outdir+outdir_sub+diago+'.'+timeo+'.'+pert_dict[pert[i]]+'.npz'
fio.save(outfile,vMSETot_col=vhTot,vMSEMC_col=vhMC,vMSETr_col=vhTr,\
uMSETot_col=uhTot,uMSEMC_col=uhMC,uMSETr_col=uhTr,\
wMSEbot=whbot,wMSEtop=whtop,net_atm=net,dene_col=de)
uMSE = np.sum(uTr*MSETr*dpMon,1)/GRAV
uMSETr[yri*12+moni,:,:] = np.mean(uMSE,0)
uMSE = np.sum(MSEMon*uMon*dpMon,0)/GRAV
uMSEMC[yri*12+moni,:,:] = uMSE
wMC[yri*12+moni,:,:,:] = np.mean(omega,0)
wMSEbot[yri*12+moni,:,:] = (np.mean(omega*MSE,0)[-1,...])/GRAV
wMSEtop[yri*12+moni,:,:] = (np.mean(omega*MSE,0)[0,...])/GRAV
#%%
#mind = [6,7,8]
"""
if init:
outfile = outdir+'dim.'+pert_dict[pert[i]]+'.npz'
fio.save(outfile,lat=lat,lon=lon,phalf=phalf,land_mask=land_mask,year=yr)
"""
for flagi in range(nflag):
vhTot = pp.month_to_year(vMSETot,yr_ts,flag[flagi])#*20650/1e19
vhMC = pp.month_to_year(vMSEMC,yr_ts,flag[flagi])
vhTr = pp.month_to_year(vMSETr,yr_ts,flag[flagi])
uhTot = pp.month_to_year(uMSETot,yr_ts,flag[flagi])
uhMC = pp.month_to_year(uMSEMC,yr_ts,flag[flagi])
uhTr = pp.month_to_year(uMSETr,yr_ts,flag[flagi])
whbot = pp.month_to_year(wMSEbot,yr_ts,flag[flagi])
whtop = pp.month_to_year(wMSEtop,yr_ts,flag[flagi])
net = pp.month_to_year(net_atm,yr_ts,flag[flagi])
de = pp.month_to_year(dene,yr_ts,flag[flagi])
"""
outdir_sub='ts/'+sub_dict[flag[flagi]]+'/'
outfile = outdir+outdir_sub+diago+'.'+timeo+'.'+pert_dict[pert[i]]+'.npz'
fio.save(outfile,vMSETot_col=vhTot,vMSEMC_col=vhMC,vMSETr_col=vhTr,\
uMSETot_col=uhTot,uMSEMC_col=uhMC,uMSETr_col=uhTr,\
wMSEbot=whbot,wMSEtop=whtop,net_atm=net,dene_col=de)
for i in range(ts_ann.shape[0]):
nan_ind = np.where(np.isnan(ts_ann[i,:,:]))
land_mask[0,nan_ind[0],nan_ind[1]] = 0
ts_ann[np.where(np.isnan(ts_ann))]=0
ts_gm = np.sum(np.nansum(ts_ann*area*land_mask,-1),-1)/np.nansum(area*land_mask)
land_mask.shape = (nlat,nlon)
area.shape = (nlat,nlon)
weight = area/np.sum(area)
"""
if zm:
ts = np.mean(ts,-1)
#%%
if init:
outfile = outdir+'dim.'+obs+'.npz'
fio.save(outfile,lat=lat,lon=lon,land_mask=land_mask)
if init3d:
tmp = nc.netcdf_file(files[-1],'r',mmap=True)
pfull = tmp.variables['pfull'][:].astype(np.float64)
outfile = outdir+'dim.'+obs+'.npz'
fio.save(outfile,pfull=pfull)
ts_flag = ts.copy()
for flagi in range(nflag):
if (flag[flagi] != ''):
ts_flag = pp.month_to_year(ts,yr_ts,flag[flagi])
outdir_sub='ts/'+sub_dict[flag[flagi]]+'/'
outfile = outdir+outdir_sub+diago+'.'+timeo+'.'+obs+'.npz'
fio.save(outfile,**{varo:ts_flag})
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 25 09:49:37 2016
@author: Zhaoyi.Shen
"""
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('/home/z1s/PythonScripts')
import postprocess as pp
fdir = '/archive/Zhaoyi.Shen/fms/ulm/AM2/AM2_control_1990/gfdl.ncrc3-default-prod-openmp/pp/atmos_level/'
f = fdir+'ts/annual/16yr/atmos_level.1983-1998.netrad_toa.nc'
ncf = nc.netcdf_file(f,'r',mmap=True)
ann = ncf.variables['netrad_toa'][0,0,0].astype(np.float64)
f = fdir+'ts/monthly/16yr/atmos_level.198301-199812.netrad_toa.nc'
ncf = nc.netcdf_file(f,'r',mmap=True)
tmp = ncf.variables['netrad_toa'][:12,0,0].astype(np.float64)
mon = pp.month_to_year(tmp,np.ones(1),'annual')
sys.path.append('/home/z1s/py/lib')
import binfile_io as fio
import postprocess as pp
import numpy as np
from scipy.io import netcdf as nc
import calendar
arr1 = np.arange(1,25,1)
yr = np.array((1999,2000))
ans_key = {'annual':np.array((2382/365.,6776/366.)),\
'MAM':np.array((368/92.,1472/92.)),\
'JJA':np.array((645/92.,1749/92.)),\
'SON':np.array((910/91.,2002/91.)),\
'DJF':np.array((87/59.,1181/91.))}
for flag in ['MAM','JJA','SON','DJF']:
arr2 = pp.month_to_year(arr1,yr,flag)
ans = ans_key[flag]
if np.max(np.abs(arr2-ans)) < 1e-8:
print flag+':Correct!'
else:
print flag+':',arr2,ans
fdir = '/archive/Zhaoyi.Shen/fms/ulm/AM2/AM2_control_1990/gfdl.ncrc3-default-prod-openmp/pp/atmos_level/'
fname = fdir+'ts/annual/16yr/atmos_level.1983-1998.netrad_toa.nc'
f = nc.netcdf_file(fname,'r')
ann = f.variables['netrad_toa'][:]
fname = fdir+'ts/monthly/16yr/atmos_level.198301-199812.netrad_toa.nc'
f = nc.netcdf_file(fname,'r')
tmp = f.variables['netrad_toa'][:]
mon = pp.month_to_year(tmp,np.ones(16),'annual')
diff = np.abs(mon-ann)
import numpy as np
from scipy.io import netcdf as nc
import calendar
arr1 = np.arange(1,25,1)
yr = np.array((1999,2000))
ans_key = {'annual':np.array((2382/365.,6776/366.)),\
'MAM':np.array((368/92.,1472/92.)),\
'JJA':np.array((645/92.,1749/92.)),\
'SON':np.array((910/91.,2002/91.)),\
'DJF':np.array((87/59.,1181/91.)),\
'JAS':np.array((735/92.,1839/92.)),\
'MJJASO':np.array((1380/184.,3588/184.)),\
'JJASON':np.array((1555/183.,3751/183.))}
for flag in ans_key:
arr2 = pp.month_to_year(arr1,yr,flag)
ans = ans_key[flag]
if np.max(np.abs(arr2-ans)) < 1e-8:
print flag+':Correct!'
else:
print flag+':',arr2,ans
#%%
fdir = '/archive/Zhaoyi.Shen/fms/ulm/AM2/AM2_control_1990/gfdl.ncrc3-default-prod-openmp/pp/atmos_level/'
fname = fdir+'ts/annual/16yr/atmos_level.1983-1998.netrad_toa.nc'
f = nc.netcdf_file(fname,'r')
ann = f.variables['netrad_toa'][:]
f.close()
fname = fdir+'ts/monthly/16yr/atmos_level.198301-199812.netrad_toa.nc'
f = nc.netcdf_file(fname,'r')
tmp = f.variables['netrad_toa'][:]
f.close()